Algorithmic Trading & Indicators Beginner

Calculate Maximum Drawdown, Sharpe Ratio & Calmar Ratio in Python

Evaluate trading strategy performance in Python by computing Peak-to-Trough Maximum Drawdown, Annualized Sharpe Ratio, and Calmar Ratio from daily returns.

#pandas#numpy

Problem Statement

Evaluating a strategy solely by net profit hides catastrophic drawdowns. This script takes a series of daily returns or equity curve values and computes institutional risk-adjusted performance metrics.

Formula / Core Logic

Drawdown_t = (Equity_t - Running_Peak_t) / Running_Peak_t Sharpe Ratio = (Mean_Return / Std_Return) * sqrt(252)

Installation

pip install pandas numpy

calculate-max-drawdown-portfolio-python.py

import pandas as pd
import numpy as np

def portfolio_risk_metrics(returns_series, risk_free_rate=0.05):
    equity = (1 + returns_series).cumprod()
    running_peak = equity.cummax()
    drawdown = (equity - running_peak) / running_peak
    max_dd = drawdown.min()
    
    excess_ret = returns_series - (risk_free_rate / 252)
    sharpe = np.sqrt(252) * (excess_ret.mean() / returns_series.std())
    cagr = (equity.iloc[-1] ** (252 / len(returns_series))) - 1
    calmar = cagr / abs(max_dd) if max_dd != 0 else np.nan
    
    return {'CAGR_Pct': round(cagr * 100, 2), 'Max_Drawdown_Pct': round(max_dd * 100, 2), 'Sharpe_Ratio': round(sharpe, 2), 'Calmar_Ratio': round(calmar, 2)}

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Frequently Asked Questions

Q: What is considered a good Calmar Ratio?

A Calmar Ratio above 1.5 indicates strong annualized returns relative to the worst historical drawdown.